scholarly journals MICRA-Net: MICRoscopy Analysis Neural Network to solve detection, classification, and segmentation from a single simple auxiliary task

2021 ◽  
Author(s):  
Anthony Bilodeau ◽  
Constantin V.L. Delmas ◽  
Martin Parent ◽  
Paul De Koninck ◽  
Audrey Durand ◽  
...  
Keyword(s):  
Neural Network ◽  
Quantitative Analysis ◽  
High Throughput ◽  
Semantic Segmentation ◽  
Feature Maps ◽  
Expert Annotation ◽  
Microscopy Analysis ◽  
Weakly Supervised ◽  
Segmentation Task ◽  
Microscopy Images

High throughput quantitative analysis of microscopy images presents a challenge due to the complexity of the image content and the difficulty to retrieve precisely annotated datasets. In this paper we introduce a weakly-supervised MICRoscopy Analysis neural network (MICRA-Net) that can be trained on a simple main classification task using image-level annotations to solve multiple the more complex auxiliary semantic segmentation task and other associated tasks such as detection or enumeration. MICRA-Net relies on the latent information embedded within a trained model to achieve performances similar to state-of-the-art fully-supervised learning. This learnt information is extracted from the network using gradient class activation maps, which are combined to generate detailed feature maps of the biological structures of interest. We demonstrate how MICRA-Net significantly alleviates the Expert annotation process on various microscopy datasets and can be used for high-throughput quantitative analysis of microscopy images.

scholarly journals MICRA-Net: MICRoscopy Analysis Neural Network to solve detection, classification, and segmentation from a single simple auxiliary task

2020 ◽  
Author(s):  
Flavie Lavoie-Cardinal ◽  
Anthony Bilodeau ◽  
Constantin Delmas ◽  
Martin Parent ◽  
Paul De Koninck ◽  
...  
Keyword(s):  
Neural Network ◽  
Quantitative Analysis ◽  
High Throughput ◽  
State Of The Art ◽  
Feature Maps ◽  
Annotation Process ◽  
Expert Annotation ◽  
Microscopy Analysis ◽  
Weakly Supervised ◽  
Microscopy Images

Abstract High throughput quantitative analysis of microscopy images presents a challenge due to the complexity of the image content and the difficulty to retrieve precisely annotated datasets. In this paper we introduce a weakly-supervised MICRoscopy Analysis neural network (MICRA-Net) that can be trained on a simple main classification task using image-level annotations to solve multiple more complex auxiliary tasks, such as segmentation, detection, and enumeration. MICRA-Net relies on the latent information embedded within a trained model to achieve performances similar to state-of-the-art fully-supervised learning. This learnt information is extracted from the network using gradient class activation maps, which are combined to generate precise feature maps of the biological structures of interest. We demonstrate how MICRA-Net significantly alleviates the expert annotation process on various microscopy datasets and can be used for high-throughput quantitative analysis of microscopy images.

scholarly journals SEMANTIC SEGMENTATION OF AERIAL IMAGERY VIA MULTI-SCALE SHUFFLING CONVOLUTIONAL NEURAL NETWORKS WITH DEEP SUPERVISION

2018 ◽  
Vol IV-1 ◽  
pp. 29-36 ◽  
Author(s):  
K. Chen ◽  
M. Weinmann ◽  
X. Sun ◽  
M. Yan ◽  
S. Hinz ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Semantic Segmentation ◽  
Aerial Imagery ◽  
Geometric Features ◽  
Feature Maps ◽  
Multi Scale ◽  
Intermediate Layers ◽  
Segmentation Task ◽  
The Impact

<p><strong>Abstract.</strong> In this paper, we address the semantic segmentation of aerial imagery based on the use of multi-modal data given in the form of true orthophotos and the corresponding Digital Surface Models (DSMs). We present the Deeply-supervised Shuffling Convolutional Neural Network (DSCNN) representing a multi-scale extension of the Shuffling Convolutional Neural Network (SCNN) with deep supervision. Thereby, we take the advantage of the SCNN involving the shuffling operator to effectively upsample feature maps and then fuse multiscale features derived from the intermediate layers of the SCNN, which results in the Multi-scale Shuffling Convolutional Neural Network (MSCNN). Based on the MSCNN, we derive the DSCNN by introducing additional losses into the intermediate layers of the MSCNN. In addition, we investigate the impact of using different sets of hand-crafted radiometric and geometric features derived from the true orthophotos and the DSMs on the semantic segmentation task. For performance evaluation, we use a commonly used benchmark dataset. The achieved results reveal that both multi-scale fusion and deep supervision contribute to an improvement in performance. Furthermore, the use of a diversity of hand-crafted radiometric and geometric features as input for the DSCNN does not provide the best numerical results, but smoother and improved detections for several objects.</p>

scholarly journals Concrete Cracks Detection Based on FCN with Dilated Convolution

2019 ◽  
Vol 9 (13) ◽  
pp. 2686 ◽  
Author(s):  
Jianming Zhang ◽  
Chaoquan Lu ◽  
Jin Wang ◽  
Lei Wang ◽  
Xiao-Guang Yue
Keyword(s):  
Crack Detection ◽  
Receptive Fields ◽  
Semantic Segmentation ◽  
Concrete Surface ◽  
Input Image ◽  
Feature Maps ◽  
Test Set ◽  
Dilated Convolution ◽  
Fully Convolutional Networks ◽  
Segmentation Task

In civil engineering, the stability of concrete is of great significance to safety of people’s life and property, so it is necessary to detect concrete damage effectively. In this paper, we treat crack detection on concrete surface as a semantic segmentation task that distinguishes background from crack at the pixel level. Inspired by Fully Convolutional Networks (FCN), we propose a full convolution network based on dilated convolution for concrete crack detection, which consists of an encoder and a decoder. Specifically, we first used the residual network to extract the feature maps of the input image, designed the dilated convolutions with different dilation rates to extract the feature maps of different receptive fields, and fused the extracted features from multiple branches. Then, we exploited the stacked deconvolution to do up-sampling operator in the fused feature maps. Finally, we used the SoftMax function to classify the feature maps at the pixel level. In order to verify the validity of the model, we introduced the commonly used evaluation indicators of semantic segmentation: Pixel Accuracy (PA), Mean Pixel Accuracy (MPA), Mean Intersection over Union (MIoU), and Frequency Weighted Intersection over Union (FWIoU). The experimental results show that the proposed model converges faster and has better generalization performance on the test set by introducing dilated convolutions with different dilation rates and a multi-branch fusion strategy. Our model has a PA of 96.84%, MPA of 92.55%, MIoU of 86.05% and FWIoU of 94.22% on the test set, which is superior to other models.

Deep learning Q inversion from reflection seismic data with strong attenuation using an encoder-decoder convolutional neural network: an example from South China Sea

2020 ◽  
Author(s):  
Hao Zhang ◽  
Jianguang Han ◽  
Heng Zhang ◽  
Yi Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Resolution ◽  
Seismic Data ◽  
Semantic Segmentation ◽  
Attenuation Effect ◽  
Attenuation Model ◽  
Reflection Seismic ◽  
Q Model ◽  
Segmentation Task

&lt;p&gt;The seismic waves exhibit various types of attenuation while propagating through the subsurface, which is strongly related to the complexity of the earth. Anelasticity of the subsurface medium, which is quantified by the quality factor Q, causes dissipation of seismic energy. Attenuation distorts the phase of the seismic data and decays the higher frequencies in the data more than lower frequencies. Strong attenuation effect resulting from geology such as gas pocket is a notoriously challenging problem for high resolution imaging because it strongly reduces the amplitude and downgrade the imaging quality of deeper events. To compensate this attenuation effect, first we need to accurately estimate the attenuation model (Q). However, it is challenging to directly derive a laterally and vertically varying attenuation model in depth domain from the surface reflection seismic data. This research paper proposes a method to derive the anomalous Q model corresponding to strong attenuative media from marine reflection seismic data using a deep-learning approach, the convolutional neural network (CNN). We treat Q anomaly detection problem as a semantic segmentation task and train an encoder-decoder CNN (U-Net) to perform a pixel-by-pixel prediction on the seismic section to invert a pixel group belongs to different level of attenuation probability which can help to build up the attenuation model. The proposed method in this paper uses a volume of marine 3D reflection seismic data for network training and validation, which needs only a very small amount of data as the training set due to the feature of U-Net, a specific encoder-decoder CNN architecture in semantic segmentation task. Finally, in order to evaluate the attenuation model result predicted by the proposed method, we validate the predicted heterogeneous Q model using de-absorption pre-stack depth migration (Q-PSDM), a high-resolution depth imaging result with reasonable compensation is obtained.&lt;/p&gt;

scholarly journals ORTHOSEG: A DEEP MULTIMODAL CONVOLUTONAL NEURAL NETWORK ARCHITECTURE FOR SEMANTIC SEGMENTATION OF ORTHOIMAGERY

2018 ◽  
Vol XLII-5 ◽  
pp. 621-628 ◽  
Author(s):  
P. Bodani ◽  
K. Shreshtha ◽  
S. Sharma
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Multiple Scales ◽  
Semantic Segmentation ◽  
Effective Field ◽  
Surface Model ◽  
Training Procedure ◽  
Feature Maps ◽  
Neural Network Architecture ◽  
Wide Range

<p><strong>Abstract.</strong> This paper addresses the task of semantic segmentation of orthoimagery using multimodal data e.g. optical RGB, infrared and digital surface model. We propose a deep convolutional neural network architecture termed OrthoSeg for semantic segmentation using multimodal, orthorectified and coregistered data. We also propose a training procedure for supervised training of OrthoSeg. The training procedure complements the inherent architectural characteristics of OrthoSeg for preventing complex co-adaptations of learned features, which may arise due to probable high dimensionality and spatial correlation in multimodal and/or multispectral coregistered data. OrthoSeg consists of parallel encoding networks for independent encoding of multimodal feature maps and a decoder designed for efficiently fusing independently encoded multimodal feature maps. A softmax layer at the end of the network uses the features generated by the decoder for pixel-wise classification. The decoder fuses feature maps from the parallel encoders locally as well as contextually at multiple scales to generate per-pixel feature maps for final pixel-wise classification resulting in segmented output. We experimentally show the merits of OrthoSeg by demonstrating state-of-the-art accuracy on the ISPRS Potsdam 2D Semantic Segmentation dataset. Adaptability is one of the key motivations behind OrthoSeg so that it serves as a useful architectural option for a wide range of problems involving the task of semantic segmentation of coregistered multimodal and/or multispectral imagery. Hence, OrthoSeg is designed to enable independent scaling of parallel encoder networks and decoder network to better match application requirements, such as the number of input channels, the effective field-of-view, and model capacity.</p>

scholarly journals Fully Convolutional Neural Network with Augmented Atrous Spatial Pyramid Pool and Fully Connected Fusion Path for High Resolution Remote Sensing Image Segmentation

2019 ◽  
Vol 9 (9) ◽  
pp. 1816 ◽  
Author(s):  
Guangsheng Chen ◽  
Chao Li ◽  
Wei Wei ◽  
Weipeng Jing ◽  
Marcin Woźniak ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Dilated Convolution ◽  
Segmentation Task ◽  
Fully Connected ◽  
Spatial Pyramid

Recent developments in Convolutional Neural Networks (CNNs) have allowed for the achievement of solid advances in semantic segmentation of high-resolution remote sensing (HRRS) images. Nevertheless, the problems of poor classification of small objects and unclear boundaries caused by the characteristics of the HRRS image data have not been fully considered by previous works. To tackle these challenging problems, we propose an improved semantic segmentation neural network, which adopts dilated convolution, a fully connected (FC) fusion path and pre-trained encoder for the semantic segmentation task of HRRS imagery. The network is built with the computationally-efficient DeepLabv3 architecture, with added Augmented Atrous Spatial Pyramid Pool and FC Fusion Path layers. Dilated convolution enlarges the receptive field of feature points without decreasing the feature map resolution. The improved neural network architecture enhances HRRS image segmentation, reaching the classification accuracy of 91%, and the precision of recognition of small objects is improved. The applicability of the improved model to the remote sensing image segmentation task is verified.

scholarly journals JMLNet: Joint Multi-Label Learning Network for Weakly Supervised Semantic Segmentation in Aerial Images

2020 ◽  
Vol 12 (19) ◽  
pp. 3169
Author(s):  
Rongxin Guo ◽  
Xian Sun ◽  
Kaiqiang Chen ◽  
Xiao Zhou ◽  
Zhiyuan Yan ◽  
...  
Keyword(s):  
Common Knowledge ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Combination Strategy ◽  
Research Attention ◽  
Significant Saving ◽  
Learning Network ◽  
Weakly Supervised ◽  
Segmentation Task

Weakly supervised semantic segmentation in aerial images has attracted growing research attention due to the significant saving in annotation cost. Most of the current approaches are based on one specific pseudo label. These methods easily overfit the wrongly labeled pixels from noisy label and limit the performance and generalization of the segmentation model. To tackle these problems, we propose a novel joint multi-label learning network (JMLNet) to help the model learn common knowledge from multiple noisy labels and prevent the model from overfitting one specific label. Our combination strategy of multiple proposals is that we regard them all as ground truth and propose three new multi-label losses to use the multi-label guide segmentation model in the training process. JMLNet also contains two methods to generate high-quality proposals, which further improve the performance of the segmentation task. First we propose a detection-based GradCAM (GradCAMD) to generate segmentation proposals from object detectors. Then we use GradCAMD to adjust the GrabCut algorithm and generate segmentation proposals (GrabCutC). We report the state-of-the-art results on the semantic segmentation task of iSAID and mapping challenge dataset when training with bounding boxes annotations.

A Weakly Supervised Semantic Segmentation Network by Aggregating Seed Cues: The Multi-Object Proposal Generation Perspective

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-19
Author(s):  
Junsheng Xiao ◽  
Huahu Xu ◽  
Honghao Gao ◽  
Minjie Bian ◽  
Yang Li
Keyword(s):  
Image Classification ◽  
Real World ◽  
Semantic Segmentation ◽  
Feature Maps ◽  
High Confidence ◽  
Deep Convolutional Neural Networks ◽  
Object Proposal ◽  
Initial Location ◽  
Weakly Supervised ◽  
High Level

Weakly supervised semantic segmentation under image-level annotations is effectiveness for real-world applications. The small and sparse discriminative regions obtained from an image classification network that are typically used as the important initial location of semantic segmentation also form the bottleneck. Although deep convolutional neural networks (DCNNs) have exhibited promising performances for single-label image classification tasks, images of the real-world usually contain multiple categories, which is still an open problem. So, the problem of obtaining high-confidence discriminative regions from multi-label classification networks remains unsolved. To solve this problem, this article proposes an innovative three-step framework within the perspective of multi-object proposal generation. First, an image is divided into candidate boxes using the object proposal method. The candidate boxes are sent to a single-classification network to obtain the discriminative regions. Second, the discriminative regions are aggregated to obtain a high-confidence seed map. Third, the seed cues grow on the feature maps of high-level semantics produced by a backbone segmentation network. Experiments are carried out on the PASCAL VOC 2012 dataset to verify the effectiveness of our approach, which is shown to outperform other baseline image segmentation methods.

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